We reproduce the experimental setting of [1]} who also study fairness in reciprocal recommendation. We simulate a friend recommendation task from the Higgs Twitter dataset [2]}, which contains directed follower links on the social network Twitter. We consider a mutual follow as a “match”, and we keep users having at least 20 matches, resulting in a subset of \(13k\)  users. We estimate mutual scores \(\mu _{ij}\)  (i.e., match probabilities) by matrix factorization.
